9 R-Powered Visualizations for Power BI Dashboards
9 R-Powered Visualizations for Power BI Dashboards
Power BI team has introduced some amazing R-based visualization plugins. Read on for some ways to make data-driven storytelling even more compelling and fun!
Join the DZone community and get the full member experience.Join For Free
Access NoSQL and Big Data through SQL using standard drivers (ODBC, JDBC, ADO.NET). Free Download
Power BI continues to introduce features and plugins to make it a one-stop shop for performing descriptive, predictive, and prescriptive analyses. As we all know, Power BI has the capability to embed R scripts and visualizations without any fuss. To make things more interesting, Microsoft's Power BI team has introduced some amazing R-based visualization plugins that are considered to be R and its associated environment's niche offerings to the data science community.
With Power BI's October 2016 version 7, R-based visual libraries were introduced. Today, we have several plugins that are going to make data-driven storytelling even more compelling and fun.
In today's article, I will share links and information related to these visual libraries.
1. Association Rules
This amazing feature has always been very close to my heart, especially when one has to uncover relationships between data that is unrelated and complex. Association rules can be a great tool to drive decisions related to promotional pricing and product placement. This feature lets the user control the algorithm parameters and visual attributes to best suit the needs.
2. Spline Chart
When it comes to visualizing and understanding the noisy data, nothing beats the spline chart. It's not always easy to understand trends and tell the hidden story behind data. The spline chart does this job splendidly, enabling the user to visualize and understand noisy scatter plot data.
3. Time Series Decomposition Chart
It's a common scenario. A practitioner has sales data from the past several months and wants to make sense of time series data. Typically, the next step would be to perform a forecast for the next time period. The decomposition of time series is a statistical method that splits a time series into several components, each representing one of the underlying processes. There are three components that are typically of interest: trend, seasonality, and noise. Time series decomposition is an essential analytics tool to understand time series components and to improve forecasts. You can control the algorithm parameters and visual attributes to suit your needs. The current visual implements the well-known "seasonal and trend decomposition using Loess" approach.
4. Funnel Plot
On occasion, we find patterns in statistical noise that lead us to incorrect conclusions about underlying data. The funnel plot helps you compare samples, and find true outliers among the measurements with varying precision. It's widely used for comparing institutional performance and medical data analysis. This visual uses a fixed effect model estimator. You can control the visual attributes to suit your needs.
Everyone is trying to make sense of and extract value from their data. In the real world, data is often not easy to separate and patterns are not usually obvious. Clustering helps you find similar groups in your data and is one of the most common tasks in data science. It provides analysts the ability to achieve better results for initiatives, as well as to understand customers and processes at a much deeper level than a human can achieve alone. This visual uses a well-known k-means clustering algorithm. You can control algorithm parameters and visual attributes to suit your needs.
6. Decision Tree
Decision trees are probably one of the most common and easily understood decision support tools. The decision tree learning automatically finds the important decision criteria to consider and uses the most intuitive and explicit visual representation. The current visual implements popular and widely used recursive partitioning tools for decision tree construction. Each leaf of the tree is labeled with a class and a probability distribution over the classes. Besides this, we use cross-validation to estimate the statistical performance of the decision tree. If the target variable is categorical or has only a few possible values, a "classification tree" is constructed, whereas if the target variable is numeric. the result of the visual is "regression tree." You can control the algorithm parameters and the visual attributes to suit your needs.
7. Correlation Plot
Correlation plots can be used to quickly find insights. It is used to investigate the dependence between multiple variables at the same time and to highlight the most correlated variables in a data table. In this visual, correlation coefficients are colored according to value. A correlation matrix can be reordered according to the degree of association between variables or can be clustered using a hierarchical clustering algorithm. The usage of this visual is very simple and intuitive.
8. Clustering the Outliers and Cluster Analysis
Everyone is trying to make sense of their data. In the real world, data is often not easy to separate, and patterns are not usually obvious. As I said previously, clustering helps you find similar groups in your data and is one of the most common tasks in data science. Finding the outliers, which are observations in your data isolated from the rest of the observations, is often a difficult analytics task by its own. This explains why density-based clustering, which finds similar groups and outliers in your data simultaneously, is one of the most common clustering algorithms.
9. Time Series Forecasting/Forecasting With ARIMA
Use forecasting today to optimize for tomorrow! Time series forecasting is the use of a model to predict future values based on previously observed values. It is one of the prime tools of any business analyst used to predict demand and inventory, budgeting, sales quotas, marketing campaigns, and procurement. Accurate forecasts lead to better decisions. The current visual implements the well-known exponential smoothing method for forecasting. The prediction is based on trend and seasonality modeling. You can control the algorithm parameters and visual attributes to suit your needs.
Published at DZone with permission of Sunil Kappal , DZone MVB. See the original article here.
Opinions expressed by DZone contributors are their own.